MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression

Neuroanatomical changes to the cortex during adolescence have been well documented using MRI, revealing ongoing cortical thinning and volume loss with age. However, the underlying cellular mechanisms remain elusive with conventional neuroimaging. Recent advances in MRI hardware and new biophysical models of tissue informed by diffusion MRI data hold promise for identifying the cellular changes driving these morphological observations. This study used ultra-strong gradient MRI to obtain high-resolution, in vivo estimates of cortical neurite and soma microstructure in sample of typically developing children and adolescents. Cortical neurite signal fraction, attributed to neuronal and glial processes, increased with age (mean R2fneurite=.53, p<3.3e-11, 11.91% increase over age), while apparent soma radius decreased (mean R2Rsoma=.48, p<4.4e-10, 1% decrease over age) across domain-specific networks. To complement these findings, developmental patterns of cortical gene expression in two independent post-mortem databases were analysed. This revealed increased expression of genes expressed in oligodendrocytes, and excitatory neurons, alongside a relative decrease in expression of genes expressed in astrocyte, microglia and endothelial cell-types. Age-related genes were significantly enriched in cortical oligodendrocytes, oligodendrocyte progenitors and Layer 5-6 neurons (pFDR<.001) and prominently expressed in adolescence and young adulthood. The spatial and temporal alignment of oligodendrocyte cell-type gene expression with neurite and soma microstructural changes suggest that ongoing cortical myelination processes contribute to adolescent cortical development. These findings highlight the role of intra-cortical myelination in cortical maturation during adolescence and into adulthood.


Context
Over the last two decades, magnetic resonance imaging (MRI) has provided invaluable insights into the developing brain, revealing ongoing cortical thinning and cortical volume loss throughout adolescence (Mills et al., 2016;Tamnes et al., 2017).However, the underlying cellular processes driving these changes are less understood.Cortical cytoarchitecture can be broadly categorised into neurites (e.g., axons, dendrites, and glial processes) and soma (e.g., neuronal, and glial cell bodies).Traditionally, synaptic pruning has been considered the primary driver of developmental changes in cortical morphology (Huttenlocher, 1979).Recent evidence, however, suggests that myelin encroachment into the grey/white matter boundary may also contribute to changes in MR contrast typically used for volumetrics, such as T1 (Natu et al., 2019).Developmental patterns of cortical myelination have been elucidated using magnetization transfer (MT) imaging (Paquola et al., 2019), and indirectly using T1w/T2w ratio (Grydeland et al., 2019).Despite these advances, how microstructural changes -specifically neurite and soma properties -contribute to these distinct morphological changes remains unclear.
Diffusion-weighted MRI (dMRI) is the main non-invasive MRI technique capable of probing the tissue microstructure, orders of magnitude smaller than the typical millimetre image resolution of structural MRI (Le Bihan et al., 2001).This microstructural imaging method is highly sensitive to the magnitude and direction of water diffusing within brain tissue.By employing biophysical models, it is possible to infer microscopic properties of different tissues, such as neurite signal fraction in the brain's white matter (Alexander et al., 2019;Zhang et al., 2012).In comparison with white matter, grey matter cytoarchitecture, broadly categorized into neurites (e.g., elongated structures such as axons, dendrites and glial processes) and soma (e.g., spherical structures such as neuronal and glial cell-bodies) is more locally complex, requiring extensions of the standard models of microstructure developed for studying the white matter.Recent hardware (Fan et al., 2022;Jones et al., 2018) and biophysical modelling (Jelescu et al., 2022;Palombo et al., 2020;Tax et al., 2020) developments have enabled diffusion-weighted microstructural quantification of soma and neurite components in the cortex in vivo.The Soma and Neurite Density Imaging (SANDI; Palombo et al. (2020)), is robust, reliable (Genc et al., 2021), clinically feasible for sufficiently short diffusion times (Schiavi et al., 2023) and has been validated in ex vivo data (Ianuş et al., 2022).
Here, we examine cortical microstructural development in a sample of children and adolescents using ultra-strong gradient dMRI to identify specific changes in neurite and soma properties with age.To identify potential cellular substrates, we analyse developmental patterns of neurite and soma microstructure alongside contemporaneous trajectories of cortical cell-type specific gene expression measured in the developing cortex using data from two independent, post-mortem databases.We reveal key developmental patterns in cortical neurite and soma architecture, highlighting the contribution of active and ongoing cortical myelination processes to the macroscale changes observed in the cortex during adolescence.

Results
We apply a framework for cortical microstructure and cell-type specific gene expression analysis (Fig 1 ) to evaluate the cellular properties underpinning human cortical microstructural development.
Figure 1: Framework for cortical microstructure and gene expression analysis.This study employs a biophysical model of cortical neurite and soma microstructure using ultra-strong gradient dMRI (Jones et al., 2018) data collected from 88 children and adolescents aged 8-19 years.Representative maps of neurite signal fraction (fneurite), soma signal fraction (fsoma), apparent soma radius (Rsoma, µm) and extracellular signal fraction (fextracellular) are shown for one 8-year-old female participant.We also analyse two human gene expression datasets (Colantuoni et al., 2011;Li et al., 2018) to estimate celltype specific and spatial (where arrows on brain render indicate a subset of regions sampled) gene expression profiles and examine their concordance with developmental patterns of cortical microstructure.

Cortical microstructure and morphology in domain-specific networks
First, we studied the repeatability of cortical microstructural estimates from the SANDI model in a sample of 6 healthy adults scanned over 5 sessions.Intra-class coefficients (ICCs) for neurite signal fraction (fneurite), soma signal fraction (fsoma) and extracellular signal fraction (fextracellular) were very high (Fig 2c) across seven domain-specific networks (mean ICC=.97, all p<.001).Apparent soma radius (Rsoma, in µm) showed lower repeatability on average (mean ICC=.92) with lower mean repeatability driven by the limbic network (ICC=.66,p=.04).
We then studied age-related patterns of cortical microstructure and morphology in a sample of 88 typically developing children and adolescents aged 8-19 years (Table S2 S1.

Unique sex and pubertal differences in the visual network
Sex differences in brain structure have been well reported, with pubertal onset playing a critical role in initiating developmental changes to morphology (Vijayakumar et al., 2018) and microstructure (Tamnes et al., 2018).We found that grey matter volume and surface area were higher in males than females (p<.005) across all brain networks (Figure S2), following known patterns of larger brain volume in males.We observed sex differences in only two microstructural measures, Rsoma and fractional anisotropy (FA; derived from the diffusion tensor at b=1000s/mm 2 ), in the visual network (Fig S2 , S3).Females had higher Using an age-prediction random forest model for each microstructural measure in the visual network, we found that Rsoma provided the most accurate age-prediction (cross-validated R 2 = .58),followed by fneurite (R 2 = .56),and fsoma (R 2 =.28).Model fitting did not converge for fextracellular.NODDI measures showed R 2 odi=.46, and R 2 vic=.36.Feature importance analysis revealed that association cortices within the visual network had the highest contribution (top 5%) to age prediction (Fig 3b,c,d).Notably, region 31a (posterior cingulate cortex) consistently influenced age prediction across multiple measures, with Rsoma contributing 63%, ODI 7% and vic 5.4%.Additional top-ranking regions included dorsal visual area, V3A (vic = 45%), lateral temporal area, TE2a (vic = 17.8 %, fneurite = 5.1%), retrosplenial cortex, RSC, (vic=7.9%),auditory association area, A5 (fneurite=5.5%),and lateral occipital area, LO3, (fsoma=5.4%).These regions (depicted in HCP-MMP1 regions overlapping visual network
For each cell-type, we quantified the spatiotemporal patterns of gene expression using PsychENCODE data by identifying the peak growth of expression in cell-specific genes.Oligodendrocyte gene expression peaked earliest in primary motor (M1), primary visual (V1) cortices, and latest in the medial frontal (MFC) cortex (Fig S8).A notable pattern emerged in which the peak expression of oligodendrocyte genes coincided with a shift in oligodendrocyte-to-astrocyte specific expression ratio.This shift, indicating a relative increase in oligodendrocyte over astrocyte cell-type gene expression, occurred around 20 years of age in M1 and V1, and after age 25 in DLPFC, ITC and MFC (Fig 4g,h).This sequence aligns with the known earlier myelination timing in sensorimotor cortices followed by prolonged myelination in the prefrontal cortex into the third decade of life (Grydeland et al., 2019;Paquola et al., 2019;Sydnor et al., 2021).Oligodendrocyte cell-type

Discussion
We combined in vivo ultra-strong gradient dMRI with independent ex vivo gene expression analyses to map tissue microstructural architecture during human development.We now discuss each of the key findings and their implications, before summarising the strengths and limitations of our study.
Although dMRI is relatively insensitive to water within the myelin sheath itself, due to its short T2 (D. K. Jones et al., 2013), the observed increase in fneurite may nevertheless reflect intra-cortical myelination.This is supported by ex vivo macaque data showing developmental increases in glial process length and complexity (Robillard et al., 2016), and an increase in the number of myelinated axons and dendrites (Fukutomi et al., 2018), which limits water exchange and leads to a greater signal contribution from inside the neurite (Jelescu et al., 2022;Olesen et al., 2022).Oligodendrocyte cell turnover in the frontal cortex is dynamic, especially in adulthood, and 10 times higher in the cortex than in the white matter (Yeung et al., 2014).OPCs can generate myelinating oligodendrocytes in adulthood, even in fully myelinated regions (Richardson et al., 2011;Young et al., 2013).Importantly, oligodendrocyte function is not restricted to myelination, rather, they also perform many critical neuronal support functions beyond myelination (Bradl & Lassmann, 2010).Together our microstructural MRI and gene-expression findings converge towards increased cortical myelination through adolescence.

Apparent Soma Radius Decreases from Childhood to Adolescence
The dMRI-derived apparent soma radius, Rsoma, decreased cortex-wide from childhood to adolescence.
Neuronal soma are much larger than glial soma, measuring ~16µm in diameter in layers 5-6 of the adult human prefrontal cortex, whereas glial soma range in diameter from 1-11µm (Rajkowska et al., 1998).Our gene expression analysis suggests specific changes in the cellular composition of the cortex with age: decreasing expression levels for astrocyte, microglia and endothelial cell-types, and (much larger) increasing expression levels for oligodendrocyte cell-types.Glial composition in the neocortex is mostly comprised of oligodendrocytes (~75%), followed by astrocytes (~20%) and a smaller prevalence of microglia (~5%) (Pelvig et al., 2008).Assuming gene expression levels are proportional to cell number/density, our observations suggest a decrease in large-soma cells (e.g., endothelial), outweighed by a larger increase in small-soma cells (e.g., oligodendrocytes).
The estimated Rsoma is dependent on the higher order moments of the soma radii distribution (i.e.skewdness and tailedness) within an MRI voxel (Olesen et al., 2022).Our own simulations of Rsoma based on known cell composition in the human brain (Keller et al., 2018) revealed a decrease in apparent soma radii with age matching our in vivo imaging observations (i.e., a 1% decrease).This would in turn lead to a reduction in the measured dMRI signal coming from water molecules fully restricted in soma, aligning with our in vivo observations of decreasing fsoma with age in the limbic, somatomotor, and dorsal attention networks.It is plausible that an increase in oligodendrocyte (Peters & Sethares, 2004), not astrocyte or microglial (Robillard et al., 2016), composition could concomitantly result in a smaller average soma radii and lower soma signal fraction in the cortex through adolescence to early adulthood.

Sex Differences in Microstructural Properties
Females have larger apparent soma radii than males, and fsoma and fextracellular varies with pubertal stage in the visual network (Fig 3a).Pubertal hormones can stimulate apoptosis (seen in female rat visual cortex; Nunez et al. (2002)), which could explain the lower fsoma as puberty progresses in females.Selective neuronal cell death with unchanged glial cell number can also occur during puberty in the medial prefrontal cortex (Markham et al., 2007;Willing & Juraska, 2015), however we did not observe any sex or pubertal differences in microstructure of the frontal cortex.

Extracellular Signal Fraction and Myelination
In dMRI, myelin thickening can decrease the extracellular signal fraction, due to less physical space in the extracellular matrix (Jelescu et al., 2016;Derek K Jones et al., 2013).Age-related decreases in fextracellular were confined to the visual network and orbito-frontal and inferior frontal cortices.Comprehensive evaluation of the myelin content is warranted to confirm the contributions of intracortical myelination to developmental changes in cortical morphology (Mancini et al., 2020).

Spatiotemporal Patterns of Gene Expression
Peak oligodendrocyte cell-type gene expression progressed along the S-A axis, with earliest peaks in M1 and V1, and latest in MFC (Fig S8), mirroring spatial patterns of peak fneurite (Fig S7).This also coincided with a relative age-related decrease in astrocyte cell-type gene expression (Fig 5g) consistent with earlylife maturation of astrocytes (Bushong et al., 2004;Cahoy et al., 2008).The S-A developmental axis describes a maturation process from lower-order, primary sensory and motor (unimodal) cortices to higher-order transmodal association cortices, which support complex neurocognitive, and socioemotional functions (Margulies et al., 2016;Sydnor et al., 2021).Prolonged maturation of the pre-frontal cortex has been reported with lower myelin content in fronto-polar cortex compared with visual or somatomotor regions from childhood to adulthood (Miller et al., 2012) indicating later myelination timing.Within the frontal cortex, age-related patterns of microstructural neurite signal fraction and soma radius were prolonged in the MFC and DLPFC (Fig 5c-e).This reflects the value of estimating in vivo neurite signal fraction as these developmental hierarchies have been reproduced across various modalities (Burt et al., 2018;Gao et al., 2020;Satterthwaite et al., 2014;Sydnor et al., 2021;Vaishnavi et al., 2010;Wagstyl et al., 12 2015), particularly when considering the regions reaching peak maturation earliest and latest.Overall, our combined imaging genetic analyses supports the evidence of an orderly and hierarchical progression of intracortical myelination.

Implications for Cortical Thinning
A recent study showed that cortical thinning during development is associated with genes expressed predominantly in astrocytes, microglia, excitatory and inhibitory neurons (Zhou et al., 2023).We observed faster cortical thinning of default-mode and visual networks, consistent with previous studies (Ball, Seidlitz, Beare, et al., 2020;Zhou et al., 2023).Apparent thinning may be a result of the macrostructural shift in the boundary between grey matter and white matter, in this scenario due to myelin encroachment into the cortex (Mournet et al., 2020;Natu et al., 2019).The microstructural composition of the grey matter itself may be better studied by the biophysical models used here.

Strengths and limitations
Several methodological advancements have advanced the understanding of underlying compositional changes to cortical microstructure across development in our study.Using in vivo microstructural imaging with ultra-strong gradients (Gmax=300 mT/m; Jones et al. ( 2018)), we achieved sensitivity to micrometerlevel imaging contrast with significant SNR improvements over clinical MRI scanners (Raven et al., 2023).
Although we used a specialised system, recent advancements have enabled these measurements on more accessible, lower-gradient strength MRI systems (e.g.Gmax≥80mT/m; Schiavi et al. ( 2023)).
Combined with two ex vivo gene expression data sets sampled from the human brain, we provide compelling evidence in favour of a framework for monitoring intra-cortical cellular composition in vivo.
Further work should evaluate in vivo imaging acquisition techniques and models that account for water exchange, which can influence biophysical modelling of grey matter compartments.
Our observation of oligodendrocyte-specific gene expression increasing towards adulthood indicates the value of imaging a broader age range of young adults to fully assess trajectories of in vivo microstructural properties.It is also important to recognise that gene expression patterns do not necessarily correlate with cellular density.Histopathological confirmation is needed to verify cell size and density with biophysical signal fractions, as well as their relevancy to functional gene expression patterns.
Overall, our study provides novel in vivo evidence of distinct developmental differences in neurite and soma architecture, aligning with cell-type specific gene expression patterns observed in ex vivo human data.This provides a window into the role of intracortical myelination through adolescence, and how it shapes the developmental patterns of cortical microstructure in vivo.

Imaging set 4.1.1. Participant characteristics
We included a sample of 88 typically developing children aged 8-19 years recruited as part of the Cardiff University Brain Research Imaging Centre (CUBRIC) Kids study.The study was approved by the School of Psychology ethics committee at Cardiff University.Participants and their parents/guardians were recruited via public outreach events.Written informed consent was obtained from the primary caregiver of each child participating in the study, and adolescents aged 16-19 years also provided written consent.
Children were excluded from the study if they had non-removable metal implants or reported history of a major head injury or epilepsy.
We administered a survey to parents of all participants, and to children aged 11-19 years.The Strengths and Difficulties Questionnaire (SDQ) was used to assess emotional/behavioural difficulties (Goodman, 1997).The Pubertal Development Scale (Petersen et al., 1988) was used to determine pubertal stage (PDSS; Shirtcliff et al. (2009)).Additionally, we measured each child's height and weight to calculate their Body-Mass index (BMI) (kg/m 2 ).
All children and adolescents underwent in-person training to prepare them for the MRI procedure using a dedicated mock MRI scanner.This protocol was 15-30 minutes long, and designed to familiarise them to the scanner environment, to minimize head motion during the scan.All procedures were completed in accordance with the Declaration of Helsinki.
Repeatability data: Six healthy adults aged 24-30 years (3 female) were scanned five times in the span of two weeks (Koller et al., 2021) on the same Connectom system.Multi-shell dMRI data were collected as above, with an additional 20 diffusion directions acquired at b=200 s/ mm 2 .
Morphological measures including cortical thickness (CTh, mm), surface area (SA, mm 2 ), and grey matter volume (GMvol, mm 3 ) were computed at the whole brain, and parcel level.The analysis framework is detailed in Figure 1 and networks studied are depicted in Fig 2a.

Cortical gene expression set
Pre-processed, batch-corrected and normalised microarray and bulk RNA-seq data from postmortem human tissue samples were obtained from the BrainCloud (Colantuoni et al., 2011) (Li et al., 2018).The cortical regions sampled are summarised in Table S1.Tissue was collected after obtaining parental or next of kin consent and with approval by the institutional review boards at the Yale University School of Medicine, the National Institutes of Health, and at each institution from which tissue specimens were obtained.Tissue processing is detailed elsewhere (Ball, Seidlitz, O'Muircheartaigh, et al., 2020;Li et al., 2018).Gene expression for PsychENCODE was measured as rates per kilobase of transcript per million mapped (RPKM).Gene expression for Braincloud was preprocessed and normalized following data cleaning and regressing out technical variability (see https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30272).

In vivo imaging
We used linear regression to test for main effects of age and sex, puberty, and sex by puberty interactions.To identify the most parsimonious model and to avoid over-fitting, we used the Akaike Information Criterion (AIC) (Akaike, 1974), selecting the model with the lowest AIC.Individual general linear models were used to determine age-related differences in cortical thickness and microstructural measures in all seven Yeo networks.Evidence for an association was deemed statistically significant when p < .005(Benjamin et al., 2018).Results from linear models are presented as the normalized coefficient of variation (β) and the corresponding 95% confidence interval [lower bound, upper bound].We also report the adjusted correlation coefficient of the full model (R 2 ).
To identify important regions that contribute to age-related differences in all the studied microstructural measures, we performed age-prediction using a random forest regressor (5-fold cross-validation) for age prediction with PyCaret (www.pycaret.org).For each microstructural measure, we randomly split the data into training and validation sets using an 80-20 ratio (total N=88: 70 training; 18 testing).Then, we performed feature scaling to ensure that all input variables (for each HCPMMP1 ROI) were on a similar scale prior to model fitting.The performance of the model was evaluated on the validation dataset.Finally, the features with the largest weight coefficients were extracted to identify specific cortical regions where variance in cortical microstructure was associated with age-related changes.

Gene expression profiles
To identify genes differentially expressed over age (pFDR<.05),we modelled age-related changes in normalised expression in all available postnatal tissue samples using nonlinear generalised additive models with thin plate splines (k=5) (Wood, 2003) in R.

BrainCloud
The relationship between normalised gene expression and age was modelled with a nonlinear general additive model (GAM) using a penalised thin-plate spline with a maximum 5 knots: M1a.gam(expression ~ 1 + s(age, k=5, bs='tp') Note that the available BrainCloud data are already preprocessed to remove variance due to batch and sample effects (see https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30272).

PsychENCODE
We repeated the above models, now with a measure of RNA integrity (RIN) as a confounder, and gene expression defined as log2(RPKM).First, we included region as an additional factor to account for spatial variation across the cortex and included donor ID as a random effect to account for repeated samples from the same specimen.We calculated measures of goodness of fit using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for all gene models.Using a set of independent single-cell RNA studies of the human cortex (see Ball, Seidlitz et al. (2020) for details), we identified genes exhibiting differential expression across various cortical cells-types, including excitatory neurons, inhibitory neurons, oligodendrocytes, microglia, astrocytes, and endothelial cells.We then compiled gene lists for each cell-type, comprising genes that are both differentially expressed by that cell-type, and uniquely expressed by that cell-type.Mean trajectories across all cortical regions sampled were computed for each cell-type.
After identifying age-related genes, we entered our list to an independent cell-type specific expression analysis (CSEA; Xu et al. (2014)) to elucidate: 1) if genes were enriched for specific cell-types, and 2) in which developmental period was gene expression highest.

Simulations
We performed numerical simulations using realistic cell counts to explain the observed trends in Rsoma derived from in vivo dMRI data.We modelled the variability in cell body sizes within an MRI voxel by generating distributions of radii for microglia, astrocytes, oligodendrocytes, neurons, and endothelial cells.
For each cell-type, we assumed the observed age-related slope of gene expression was proportional to the number of cells within an MRI voxel.Based on realistic cell counts outlined in Keller et al. (2018), we set the number of cells in mm 3 as follows: Nmicro = 6,500; Nastro = 15,700; Noligo = 12,500; Nneuro = 92,000; Nendo = Nneuro*.35(Ventura-Antunes et al., 2022).For each cell type, we generated random samples of radii based on the specified cell counts assuming a Gaussian distribution with cell-type specific baseline mean and standard deviation: microglia = 2.0±0.5 µm; astrocytes and oligodendrocytes = 5.5±1.5 µm; neurons = 8.0±2.0 µm for neurons and 9.0±0.5 µm for endothelial.The resulting radii were concatenated to form a comprehensive distribution and the MR apparent soma radius Rsoma estimated as # Fig 3c) represent cortical endpoints of developmentally sensitive tracts, identified through tractography, such as the posterior corpus callosum, cingulum, and inferior longitudinal fasciculus (Fig 3d).

Figure 2 :
Figure 2: Developmental patterns of MRI-derived cortical morphology and microstructure: (a) regions in atlas used to derive domain-specific networks (Yeo et al., 2011) overlaid on a representative participant; (b) developmental patterns of cortical morphology and microstructure averaged across the cortical ribbon; (c) demonstration of high repeatability of SANDI measures in six adults scanned over 5 time-points within two weeks; (d) network-wide patterns of microstructure and morphology, indicating age-related increases in neurite fraction and reductions in cortical thickness, apparent soma radius, soma fraction and extracellular fraction.Significant age relationships (p<.005) are annotated (*).Abbreviations: CTh: cortical thickness, in mm; fextracellular: extracellular signal fraction; fneurite: neurite signal fraction; fsoma: soma signal fraction; GM: grey matter; ICC: intra-class coefficient; Rsoma: apparent soma radius, in µm.

Figure 3 :
Figure 3: Developmental patterns of microstructure in the visual network.(a) Sex differences in apparent soma radius, and sex by puberty interactions for soma and extracellular signal fractions.(b) Feature importance of regions overlapping visual network (Glasser et al., 2016) to brain age estimation; top ranking regions with a weighting >5% (width of coloured bin) are in white text and accuracy of prediction model is represented (as R 2 ) on the leftmost point of the bar plot.(c) Top ranking regions overlaid on a representative participant, coloured by labels in (b).(d) White matter pathways derived from tractography connecting cortical endpoints identified in age prediction analysis, such as the cingulum, posterior corpus callosum (CC) and inferior longitudinal fasciculus (ILF) which traverse regions in (c).
S5).These genes were prominently expressed across developmental stages in childhood adolescence, and young adulthood (Fig S6, Fig 4c).The number (Fig 4d) and proportion (Fig 4e) of age-related genes expressed by oligodendrocytes increased significantly in adolescence and young adulthood (Fig 4d,e).These included

Figure 4 :
Figure 4: Developmental trajectories of cell-type specific gene expression.Data shown for samples aged 0-30 years from: (a) BrainCloud (Z-score), and (b) PsychENCODE (expressed in log2-reads-per-kilobase of transcript per million (log2RPKM)) datasets, demeaned to account for overall higher expression in some cell-types.Age effects were modelled in all postnatal samples to maximise sample size.Grey shaded areas highlight the age range of the microstructural imaging cohort (8-19 years) for visual comparison of developmental profiles.(c) SEA results(Xu et al., 2014) showing significant enrichment of age-related genes through adolescence and adulthood, where hexagon size scales with enrichment (overlap) of age-related genes in genes expressed by each cell type, and darker rings indicate significant associations at pFDR<.001 with inner rings indicating high cell specificity.Age-related genes overlapping postnatal developmental stages are shown as (d) total number of genes, and (e) proportion of genes, indicating an increase in neuronal, glial and oligodendrocyte-specific genes.(f) Trajectories of glial genes overlapping the SEA and our age-genes.(g) Regional shifts in the glial cell-type expression ratio (log2RPKM) across development, with the astrocyte-to-oligodendrocyte expression ratio crossing earliest at age 20 years in primary motor and visual cortices.(h) Timing of this cross-over, with darker values indicating regions with an earlier crossing point.Note that white coloured regions are not represented in the data set.
Increases from Childhood to Adolescence Supporting our in vivo MRI findings, oligodendrocyte-specific gene expression increased with age (Fig 5a,b), aligning with previous observations in independent data (Paquola et al., 2019).Age-related genes were also enriched in cortical neurons (layers 5 and 6) and OPCs (Fig S6).The concordance between the human gene expression analysis (Fig S5) and the CSEA analysis based on mouse transcriptomic profiling (Fig S6; Xu et al. (2014)) indicates conservation of myelination processes via cortical oligodendrocytes.